Stroke is among the leading causes of long-term disability and most strokes damage the corticospinal tract (CST) that is intimately involved in motor control ofthe arm. However, post-stroke rehabilitation of arm function is lacking both understanding of the underiying neural mechanisms and quantitative objective measures of impairment. The long-term goal ofthe proposed research is to determine the mechanism of neural compensation forthe complex limb dynamics. The objective in this project is to determine the contribution of the CST to the control of passive torques, which represent a significant aspect of limb dynamics. Our approach will be to use transcranial magnetic stimulation and diffusion tensor imaging to measure the excitability and structural integrity of the CST that accompanies goal-directed arm movements under different force constraints, such as those imposed by interaction torques and gravity. The feasibility of this approach is based on our strong preliminary data, in which we have established that the CST is involved in the compensation for interaction torques, and that post-stroke impairment is accompanied by changes in passive torques. To achieve the overall objective, three Specific Aims are proposed: 1) to determine the contribution of CST to the control of passive torques in healthy subjects; 2) to determine whether post-stroke movement deficits are due to the impaired control of passive torques by the CST; 3) to foster career development through mentoring interactions with leaders In the field. We expect that achieving these aims will provide a model of the CST activity that explains how the neural motor system compensates for internal and external forces. This research is significant, because the obtained model will help answer the question of how the CST is involved in solving muscle redundancy. We expect that obtained knowledge would further our understanding of how the human neuromuscular system produces accurate movement in presence of unpredictable environment. Results of this research also have the potential to increase the effectiveness of the current methods of robot-assisted rehabilitation and help develop novel objective diagnostic measures for predicting recovery potential and therapeutic effectiveness.